Factors influencing intentions to stay and retention of nurse managers: a systematic review
Bibliographic record
Abstract
AIMS: This systematic review aimed to explore factors known to influence intentions to stay and retention of nurse managers in their current position. BACKGROUND: Retaining staff nurses and recruiting nurses to management positions are well documented; however, there is sparse research examining factors that influence retention of nurse managers. EVALUATIONS: Thirteen studies were identified through a systematic search of the literature. Eligibility criteria included both qualitative and quantitative studies that examined factors related to nurse manager intentions to stay and retention. Quality assessments, data extraction and analysis were completed on all studies included. Twenty-one factors were categorized into three major categories: organizational, role and personal. KEY ISSUES: Job satisfaction, organizational commitment, organizational culture and values, feelings of being valued and lack of time to complete tasks leading to work/life imbalance, were prominent across all categories. CONCLUSION: These findings suggest that intentions to stay and retention of nurse managers are multifactoral. However, lack of robust literature highlights the need for further research to develop strategies to retain nurse managers. ImplICATIONS FOR NURSE MANAGEMENT: Health-care organizations and senior decision-makers should feel a responsibility to support front-line managers in relation to workload and span of control, and in understanding work/life balance issues faced by managers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".